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GEN: Why did QIAGEN Digital Insights decide to move into the single-cell analysis market, and what technological roadblocks do you think need to be addressed?
Dr. Pearson: QIAGEN Digital Insights is a leading provider of bioinformatics solutions for analysis, interpretation, and data science. Our customers are increasingly moving toward single-cell technologies, and they need robust bioinformatics solutions.
The key roadblock is the sheer amount of data and the variety of algorithms required to analyze that information, and bring it together into a digestible and interpretable format. Single-cell data are massive in terms of cell numbers and data size. This requires more computational resources and storage, more efficient algorithms, and in many cases, fundamentally different algorithms, as well as new ways to visualize and explore the data.
GEN: How does QIAGEN plan to assist clients in handling and dealing with this massive amount of data?
Dr. Pearson: We are taking several approaches. We have software dedicated to the analysis of clients’ own data. We have solutions designed to remove the barriers to exploring curated single-cell data. We also have tools that are designed for deeper-level interpretation, that is, the biological underpinnings of what actually is going on in the cell, harnessing the power of our QIAGEN Knowledge Base.
Specifically, we have QIAGEN CLC Genomics Workbench, which provides an end-to-end solution for scientists to analyze single-cell data, including automated cell type annotation. It comes with an intuitive point-and-click interface, harnessing powerful cloud computational resources to solve the big data problem. Our new OmicSoft Single Cell Land extends our ‘omics repositories with more than half a million deeply-curated samples covering a variety of therapeutic areas. This new software allows scientists to explore across curated single-cell experiments and look for patterns of expression in studies that are relevant to their internal research efforts to validate their biomarkers.
Finally, scientists can bring their own data into QIAGEN Ingenuity Pathway Analysis (IPA) to make deeper connections between their sets of up- and down-regulated genes and integrate their data into pathways and connected networks to get biological insights into their experiments. They can also easily compare their analyses to thousands of curated bulk and single-cell data within QIAGEN IPA.
GEN: Single-cell analysis allows the study of cell-to-cell variation within a cell population, be it an organ, tissue, or cell culture. For which kinds of research is this type of information especially important?
Dr. Pearson: Tissues are rarely, if ever, a homogeneous collection of cells. The cells in a tissue are all in a different state. Within a tissue there are different cell types in various stages of the cell cycle, and there is differential maturation and activation among these different cells that make up tissue. In general, with bulk transcriptomics and bulk ’omics, you only get the average; an overall profile. You miss those specific cell dynamics.
Single-cell technology is fundamentally different because it allows unambiguous profiling of individual cells. You can detect the diversity and proportions of cell types and even detect new cell types. This is important because, beyond a generally improved understanding and resolution of development and disease, single-cell resolution is essential for detecting subtle shifts in gene expression, especially in rare cell types, which can impact disease phenotypes.
Single-cell analysis also allows scientists to understand the shifts in the proportion of cell types within a tissue. In different tumors, for example, it’s critical to understand 1) the prevalence of different cell types, like those among immune cells, and 2) the activation state of immune-cell types within tumors because this correlates strongly with treatment responsiveness, prognosis, and other clinical parameters.
To learn more, visit digitalinsights.qiagen.com